# bivariate.mixalg: EM algorithm and classification for univariate data, for... In CAMAN: Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN

 bivariate.mixalg R Documentation

## EM algorithm and classification for univariate data, for bivariate data and for meta data

Function

### Usage

``````bivariate.mixalg(obs1, obs2, type, data = NULL,
var1, var2, corr, lambda1, lambda2,
p,startk, numiter=5000, acc=1.e-7, class)``````

### Arguments

 `obs1` the first column of the observations

 `obs2` the second column of the observations

 `type` kind of data

 `data` an optional data frame

 `var1` Variance of the first column of the observations(except meta-analysis)

 `var2` Variance of the second column of the observations (except meta-analysis)

 `corr` correlation coefficient

 `lambda1` Means of the first column of the observations

 `lambda2` Means of the second column of the observations

 `p` Probability

 `startk` starting/maximal number of components. This number will be used to compute the grid in the VEM. Default is 20.

 `numiter` parameter to control the maximal number of iterations in the VEM and EM loops. Default is 5000.

 `acc` convergence criterion. Default is 1.e-7

 `class` classification of studies

### Examples

``````## Not run:
#1.EM and classification for bivariate data
#Examples
data(rs12363681)
test <- bivariate.mixalg(obs1=x, obs2=y, type="bi",
lambda1=0, lambda2=0, p=0,
data=rs12363681, startk=20, class="TRUE")
#scatter plot with ellipse
plot(test)
#scatter plot without ellipse
plot(test, ellipse = FALSE)
#2.EM and classification for meta data
#Examples
data(CT)
bivariate.mixalg(obs1=logitTPR, obs2=logitTNR,
var1=varlogitTPR, var2=varlogitTNR,
type="meta", lambda1=0, lambda2=0,
p=0,data=CT,startk=20,class="TRUE")

## End(Not run)
``````

CAMAN documentation built on April 11, 2023, 6:08 p.m.